Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations7385
Missing cells0
Missing cells (%)0.0%
Duplicate rows999
Duplicate rows (%)13.5%
Total size in memory2.6 MiB
Average record size in memory374.2 B

Variable types

Categorical4
Text1
Numeric7

Alerts

Dataset has 999 (13.5%) duplicate rowsDuplicates
CO2 Emissions(g/km) is highly overall correlated with Cylinders and 5 other fieldsHigh correlation
Cylinders is highly overall correlated with CO2 Emissions(g/km) and 6 other fieldsHigh correlation
Engine Size(L) is highly overall correlated with CO2 Emissions(g/km) and 5 other fieldsHigh correlation
Fuel Consumption City (L/100 km) is highly overall correlated with CO2 Emissions(g/km) and 5 other fieldsHigh correlation
Fuel Consumption Comb (L/100 km) is highly overall correlated with CO2 Emissions(g/km) and 5 other fieldsHigh correlation
Fuel Consumption Comb (mpg) is highly overall correlated with CO2 Emissions(g/km) and 5 other fieldsHigh correlation
Fuel Consumption Hwy (L/100 km) is highly overall correlated with CO2 Emissions(g/km) and 5 other fieldsHigh correlation
Make is highly overall correlated with CylindersHigh correlation

Reproduction

Analysis started2025-08-18 08:50:18.269914
Analysis finished2025-08-18 08:50:22.722807
Duration4.45 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Make
Categorical

High correlation 

Distinct42
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size455.5 KiB
FORD
628 
CHEVROLET
588 
BMW
527 
MERCEDES-BENZ
 
419
PORSCHE
 
376
Other values (37)
4847 

Length

Max length13
Median length11
Mean length6.1439404
Min length3

Characters and Unicode

Total characters45373
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowACURA
2nd rowACURA
3rd rowACURA
4th rowACURA
5th rowACURA

Common Values

ValueCountFrequency (%)
FORD 628
 
8.5%
CHEVROLET 588
 
8.0%
BMW 527
 
7.1%
MERCEDES-BENZ 419
 
5.7%
PORSCHE 376
 
5.1%
TOYOTA 330
 
4.5%
GMC 328
 
4.4%
AUDI 286
 
3.9%
NISSAN 259
 
3.5%
JEEP 251
 
3.4%
Other values (32) 3393
45.9%

Length

2025-08-18T14:20:22.797038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ford 628
 
8.3%
chevrolet 588
 
7.8%
bmw 527
 
7.0%
mercedes-benz 419
 
5.6%
porsche 376
 
5.0%
toyota 330
 
4.4%
gmc 328
 
4.3%
audi 286
 
3.8%
nissan 259
 
3.4%
jeep 251
 
3.3%
Other values (35) 3555
47.1%

Most occurring characters

ValueCountFrequency (%)
E 4807
 
10.6%
O 3608
 
8.0%
A 3589
 
7.9%
R 3114
 
6.9%
I 2781
 
6.1%
D 2672
 
5.9%
N 2483
 
5.5%
C 2458
 
5.4%
S 2345
 
5.2%
M 2036
 
4.5%
Other values (17) 15480
34.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45373
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 4807
 
10.6%
O 3608
 
8.0%
A 3589
 
7.9%
R 3114
 
6.9%
I 2781
 
6.1%
D 2672
 
5.9%
N 2483
 
5.5%
C 2458
 
5.4%
S 2345
 
5.2%
M 2036
 
4.5%
Other values (17) 15480
34.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45373
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 4807
 
10.6%
O 3608
 
8.0%
A 3589
 
7.9%
R 3114
 
6.9%
I 2781
 
6.1%
D 2672
 
5.9%
N 2483
 
5.5%
C 2458
 
5.4%
S 2345
 
5.2%
M 2036
 
4.5%
Other values (17) 15480
34.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45373
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 4807
 
10.6%
O 3608
 
8.0%
A 3589
 
7.9%
R 3114
 
6.9%
I 2781
 
6.1%
D 2672
 
5.9%
N 2483
 
5.5%
C 2458
 
5.4%
S 2345
 
5.2%
M 2036
 
4.5%
Other values (17) 15480
34.1%

Model
Text

Distinct2053
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Memory size496.5 KiB
2025-08-18T14:20:22.956096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length41
Median length32
Mean length11.831957
Min length2

Characters and Unicode

Total characters87379
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique502 ?
Unique (%)6.8%

Sample

1st rowILX
2nd rowILX
3rd rowILX HYBRID
4th rowMDX 4WD
5th rowRDX AWD
ValueCountFrequency (%)
awd 1128
 
6.8%
ffv 592
 
3.6%
4wd 477
 
2.9%
coupe 375
 
2.3%
4x4 333
 
2.0%
s 326
 
2.0%
4matic 239
 
1.4%
cabriolet 221
 
1.3%
xdrive 215
 
1.3%
cooper 204
 
1.2%
Other values (709) 12464
75.2%
2025-08-18T14:20:23.341295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9200
 
10.5%
A 5815
 
6.7%
R 4636
 
5.3%
E 4496
 
5.1%
C 3621
 
4.1%
T 3510
 
4.0%
O 3450
 
3.9%
D 3182
 
3.6%
S 3165
 
3.6%
I 2352
 
2.7%
Other values (59) 43952
50.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 87379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9200
 
10.5%
A 5815
 
6.7%
R 4636
 
5.3%
E 4496
 
5.1%
C 3621
 
4.1%
T 3510
 
4.0%
O 3450
 
3.9%
D 3182
 
3.6%
S 3165
 
3.6%
I 2352
 
2.7%
Other values (59) 43952
50.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 87379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9200
 
10.5%
A 5815
 
6.7%
R 4636
 
5.3%
E 4496
 
5.1%
C 3621
 
4.1%
T 3510
 
4.0%
O 3450
 
3.9%
D 3182
 
3.6%
S 3165
 
3.6%
I 2352
 
2.7%
Other values (59) 43952
50.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 87379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9200
 
10.5%
A 5815
 
6.7%
R 4636
 
5.3%
E 4496
 
5.1%
C 3621
 
4.1%
T 3510
 
4.0%
O 3450
 
3.9%
D 3182
 
3.6%
S 3165
 
3.6%
I 2352
 
2.7%
Other values (59) 43952
50.3%

Vehicle Class
Categorical

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size494.8 KiB
SUV - SMALL
1217 
MID-SIZE
1133 
COMPACT
1022 
SUV - STANDARD
735 
FULL-SIZE
639 
Other values (11)
2639 

Length

Max length24
Median length21
Mean length11.587407
Min length7

Characters and Unicode

Total characters85573
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCOMPACT
2nd rowCOMPACT
3rd rowCOMPACT
4th rowSUV - SMALL
5th rowSUV - SMALL

Common Values

ValueCountFrequency (%)
SUV - SMALL 1217
16.5%
MID-SIZE 1133
15.3%
COMPACT 1022
13.8%
SUV - STANDARD 735
10.0%
FULL-SIZE 639
8.7%
SUBCOMPACT 606
8.2%
PICKUP TRUCK - STANDARD 538
7.3%
TWO-SEATER 460
 
6.2%
MINICOMPACT 326
 
4.4%
STATION WAGON - SMALL 252
 
3.4%
Other values (6) 457
 
6.2%

Length

2025-08-18T14:20:23.406108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3042
20.8%
suv 1952
13.3%
small 1628
11.1%
standard 1273
8.7%
mid-size 1186
 
8.1%
compact 1022
 
7.0%
pickup 697
 
4.8%
truck 697
 
4.8%
full-size 639
 
4.4%
subcompact 606
 
4.1%
Other values (11) 1883
12.9%

Most occurring characters

ValueCountFrequency (%)
S 8335
 
9.7%
A 7531
 
8.8%
7240
 
8.5%
C 5478
 
6.4%
T 5454
 
6.4%
- 5327
 
6.2%
M 5174
 
6.0%
I 4979
 
5.8%
L 4688
 
5.5%
U 4668
 
5.5%
Other values (14) 26699
31.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 85573
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 8335
 
9.7%
A 7531
 
8.8%
7240
 
8.5%
C 5478
 
6.4%
T 5454
 
6.4%
- 5327
 
6.2%
M 5174
 
6.0%
I 4979
 
5.8%
L 4688
 
5.5%
U 4668
 
5.5%
Other values (14) 26699
31.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 85573
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 8335
 
9.7%
A 7531
 
8.8%
7240
 
8.5%
C 5478
 
6.4%
T 5454
 
6.4%
- 5327
 
6.2%
M 5174
 
6.0%
I 4979
 
5.8%
L 4688
 
5.5%
U 4668
 
5.5%
Other values (14) 26699
31.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 85573
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 8335
 
9.7%
A 7531
 
8.8%
7240
 
8.5%
C 5478
 
6.4%
T 5454
 
6.4%
- 5327
 
6.2%
M 5174
 
6.0%
I 4979
 
5.8%
L 4688
 
5.5%
U 4668
 
5.5%
Other values (14) 26699
31.2%

Engine Size(L)
Real number (ℝ)

High correlation 

Distinct51
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1600677
Minimum0.9
Maximum8.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2025-08-18T14:20:23.475386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile1.5
Q12
median3
Q33.7
95-th percentile6
Maximum8.4
Range7.5
Interquartile range (IQR)1.7

Descriptive statistics

Standard deviation1.3541705
Coefficient of variation (CV)0.42852577
Kurtosis-0.13196328
Mean3.1600677
Median Absolute Deviation (MAD)1
Skewness0.80918099
Sum23337.1
Variance1.8337776
MonotonicityNot monotonic
2025-08-18T14:20:23.569971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 1460
19.8%
3 804
 
10.9%
3.6 536
 
7.3%
3.5 529
 
7.2%
2.5 423
 
5.7%
2.4 346
 
4.7%
1.6 302
 
4.1%
5.3 290
 
3.9%
1.8 216
 
2.9%
1.4 211
 
2.9%
Other values (41) 2268
30.7%
ValueCountFrequency (%)
0.9 3
 
< 0.1%
1 18
 
0.2%
1.2 25
 
0.3%
1.3 11
 
0.1%
1.4 211
 
2.9%
1.5 207
 
2.8%
1.6 302
 
4.1%
1.8 216
 
2.9%
2 1460
19.8%
2.1 5
 
0.1%
ValueCountFrequency (%)
8.4 5
 
0.1%
8 3
 
< 0.1%
6.8 8
 
0.1%
6.7 25
 
0.3%
6.6 29
 
0.4%
6.5 18
 
0.2%
6.4 46
 
0.6%
6.3 3
 
< 0.1%
6.2 162
2.2%
6 94
1.3%

Cylinders
Real number (ℝ)

High correlation 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6150305
Minimum3
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2025-08-18T14:20:23.641980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4
Q14
median6
Q36
95-th percentile8
Maximum16
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8283065
Coefficient of variation (CV)0.32560937
Kurtosis1.525175
Mean5.6150305
Median Absolute Deviation (MAD)2
Skewness1.1104154
Sum41467
Variance3.3427047
MonotonicityNot monotonic
2025-08-18T14:20:23.705925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4 3220
43.6%
6 2446
33.1%
8 1402
19.0%
12 151
 
2.0%
3 95
 
1.3%
10 42
 
0.6%
5 26
 
0.4%
16 3
 
< 0.1%
ValueCountFrequency (%)
3 95
 
1.3%
4 3220
43.6%
5 26
 
0.4%
6 2446
33.1%
8 1402
19.0%
10 42
 
0.6%
12 151
 
2.0%
16 3
 
< 0.1%
ValueCountFrequency (%)
16 3
 
< 0.1%
12 151
 
2.0%
10 42
 
0.6%
8 1402
19.0%
6 2446
33.1%
5 26
 
0.4%
4 3220
43.6%
3 95
 
1.3%

Transmission
Categorical

Distinct27
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size429.8 KiB
AS6
1324 
AS8
1211 
M6
901 
A6
789 
A8
490 
Other values (22)
2670 

Length

Max length4
Median length3
Mean length2.5773866
Min length2

Characters and Unicode

Total characters19034
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAS5
2nd rowM6
3rd rowAV7
4th rowAS6
5th rowAS6

Common Values

ValueCountFrequency (%)
AS6 1324
17.9%
AS8 1211
16.4%
M6 901
12.2%
A6 789
10.7%
A8 490
 
6.6%
AM7 445
 
6.0%
A9 339
 
4.6%
AS7 319
 
4.3%
AV 295
 
4.0%
M5 193
 
2.6%
Other values (17) 1079
14.6%

Length

2025-08-18T14:20:23.780985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
as6 1324
17.9%
as8 1211
16.4%
m6 901
12.2%
a6 789
10.7%
a8 490
 
6.6%
am7 445
 
6.0%
a9 339
 
4.6%
as7 319
 
4.3%
av 295
 
4.0%
m5 193
 
2.6%
Other values (17) 1079
14.6%

Most occurring characters

ValueCountFrequency (%)
A 6200
32.6%
6 3259
17.1%
S 3127
16.4%
M 1831
 
9.6%
8 1802
 
9.5%
7 1026
 
5.4%
V 576
 
3.0%
9 419
 
2.2%
5 307
 
1.6%
1 210
 
1.1%
Other values (2) 277
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19034
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 6200
32.6%
6 3259
17.1%
S 3127
16.4%
M 1831
 
9.6%
8 1802
 
9.5%
7 1026
 
5.4%
V 576
 
3.0%
9 419
 
2.2%
5 307
 
1.6%
1 210
 
1.1%
Other values (2) 277
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19034
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 6200
32.6%
6 3259
17.1%
S 3127
16.4%
M 1831
 
9.6%
8 1802
 
9.5%
7 1026
 
5.4%
V 576
 
3.0%
9 419
 
2.2%
5 307
 
1.6%
1 210
 
1.1%
Other values (2) 277
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19034
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 6200
32.6%
6 3259
17.1%
S 3127
16.4%
M 1831
 
9.6%
8 1802
 
9.5%
7 1026
 
5.4%
V 576
 
3.0%
9 419
 
2.2%
5 307
 
1.6%
1 210
 
1.1%
Other values (2) 277
 
1.5%

Fuel Type
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size418.4 KiB
X
3637 
Z
3202 
E
370 
D
 
175
N
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7385
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowZ
2nd rowZ
3rd rowZ
4th rowZ
5th rowZ

Common Values

ValueCountFrequency (%)
X 3637
49.2%
Z 3202
43.4%
E 370
 
5.0%
D 175
 
2.4%
N 1
 
< 0.1%

Length

2025-08-18T14:20:23.841352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-18T14:20:23.932394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
x 3637
49.2%
z 3202
43.4%
e 370
 
5.0%
d 175
 
2.4%
n 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
X 3637
49.2%
Z 3202
43.4%
E 370
 
5.0%
D 175
 
2.4%
N 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7385
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
X 3637
49.2%
Z 3202
43.4%
E 370
 
5.0%
D 175
 
2.4%
N 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7385
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
X 3637
49.2%
Z 3202
43.4%
E 370
 
5.0%
D 175
 
2.4%
N 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7385
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
X 3637
49.2%
Z 3202
43.4%
E 370
 
5.0%
D 175
 
2.4%
N 1
 
< 0.1%

Fuel Consumption City (L/100 km)
Real number (ℝ)

High correlation 

Distinct211
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.556534
Minimum4.2
Maximum30.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2025-08-18T14:20:24.018788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4.2
5-th percentile8
Q110.1
median12.1
Q314.6
95-th percentile19.2
Maximum30.6
Range26.4
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation3.5002741
Coefficient of variation (CV)0.27876118
Kurtosis1.196145
Mean12.556534
Median Absolute Deviation (MAD)2.2
Skewness0.80900471
Sum92730
Variance12.251919
MonotonicityNot monotonic
2025-08-18T14:20:24.104793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.8 125
 
1.7%
12.4 123
 
1.7%
11.8 120
 
1.6%
11.9 119
 
1.6%
10.6 109
 
1.5%
10.2 108
 
1.5%
11.3 107
 
1.4%
10.5 104
 
1.4%
12.1 104
 
1.4%
11.2 102
 
1.4%
Other values (201) 6264
84.8%
ValueCountFrequency (%)
4.2 5
0.1%
4.3 4
0.1%
4.4 5
0.1%
4.5 8
0.1%
4.6 9
0.1%
4.7 3
 
< 0.1%
4.8 3
 
< 0.1%
4.9 8
0.1%
5 4
0.1%
5.1 4
0.1%
ValueCountFrequency (%)
30.6 2
< 0.1%
30.3 2
< 0.1%
30.2 2
< 0.1%
30 2
< 0.1%
26.8 3
< 0.1%
26.7 1
 
< 0.1%
26.6 2
< 0.1%
26.3 1
 
< 0.1%
26.2 1
 
< 0.1%
25.7 2
< 0.1%

Fuel Consumption Hwy (L/100 km)
Real number (ℝ)

High correlation 

Distinct143
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0417062
Minimum4
Maximum20.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2025-08-18T14:20:24.196978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile6.1
Q17.5
median8.7
Q310.2
95-th percentile13.2
Maximum20.6
Range16.6
Interquartile range (IQR)2.7

Descriptive statistics

Standard deviation2.2244564
Coefficient of variation (CV)0.24602175
Kurtosis2.0089689
Mean9.0417062
Median Absolute Deviation (MAD)1.3
Skewness1.0792167
Sum66773
Variance4.9482062
MonotonicityNot monotonic
2025-08-18T14:20:24.289009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.8 209
 
2.8%
8.5 184
 
2.5%
8.7 169
 
2.3%
8.3 168
 
2.3%
8.4 162
 
2.2%
9.6 154
 
2.1%
7.7 152
 
2.1%
9.2 151
 
2.0%
7 150
 
2.0%
9 146
 
2.0%
Other values (133) 5740
77.7%
ValueCountFrequency (%)
4 4
 
0.1%
4.2 1
 
< 0.1%
4.4 3
 
< 0.1%
4.5 2
 
< 0.1%
4.6 6
 
0.1%
4.7 1
 
< 0.1%
4.8 7
0.1%
4.9 10
0.1%
5 8
0.1%
5.1 16
0.2%
ValueCountFrequency (%)
20.6 2
 
< 0.1%
20.5 5
0.1%
20.4 2
 
< 0.1%
20 1
 
< 0.1%
19.6 1
 
< 0.1%
19.3 4
0.1%
19.2 1
 
< 0.1%
18.8 2
 
< 0.1%
18.6 1
 
< 0.1%
18.5 5
0.1%

Fuel Consumption Comb (L/100 km)
Real number (ℝ)

High correlation 

Distinct181
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.975071
Minimum4.1
Maximum26.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2025-08-18T14:20:24.380627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4.1
5-th percentile7.2
Q18.9
median10.6
Q312.6
95-th percentile16.5
Maximum26.1
Range22
Interquartile range (IQR)3.7

Descriptive statistics

Standard deviation2.8925063
Coefficient of variation (CV)0.2635524
Kurtosis1.3935754
Mean10.975071
Median Absolute Deviation (MAD)1.8
Skewness0.89331572
Sum81050.9
Variance8.3665927
MonotonicityNot monotonic
2025-08-18T14:20:24.454466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.4 145
 
2.0%
8.4 136
 
1.8%
9.8 135
 
1.8%
9.1 132
 
1.8%
10.3 130
 
1.8%
8.7 128
 
1.7%
11 127
 
1.7%
9.9 125
 
1.7%
10.7 124
 
1.7%
9 121
 
1.6%
Other values (171) 6082
82.4%
ValueCountFrequency (%)
4.1 4
 
0.1%
4.2 1
 
< 0.1%
4.3 2
 
< 0.1%
4.4 2
 
< 0.1%
4.5 5
0.1%
4.7 9
0.1%
4.8 7
0.1%
4.9 6
0.1%
5 5
0.1%
5.1 12
0.2%
ValueCountFrequency (%)
26.1 2
< 0.1%
25.9 2
< 0.1%
25.8 2
< 0.1%
25.7 2
< 0.1%
23.9 1
 
< 0.1%
23 1
 
< 0.1%
22.6 4
0.1%
22.5 1
 
< 0.1%
22.2 3
< 0.1%
22.1 2
< 0.1%

Fuel Consumption Comb (mpg)
Real number (ℝ)

High correlation 

Distinct54
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.481652
Minimum11
Maximum69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2025-08-18T14:20:24.520802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile17
Q122
median27
Q332
95-th percentile39
Maximum69
Range58
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.2318792
Coefficient of variation (CV)0.263153
Kurtosis2.499369
Mean27.481652
Median Absolute Deviation (MAD)5
Skewness0.97703406
Sum202952
Variance52.300076
MonotonicityNot monotonic
2025-08-18T14:20:24.615011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 482
 
6.5%
29 470
 
6.4%
27 461
 
6.2%
22 453
 
6.1%
26 445
 
6.0%
24 399
 
5.4%
23 359
 
4.9%
31 358
 
4.8%
30 333
 
4.5%
34 327
 
4.4%
Other values (44) 3298
44.7%
ValueCountFrequency (%)
11 8
 
0.1%
12 6
 
0.1%
13 26
 
0.4%
14 30
 
0.4%
15 67
 
0.9%
16 134
1.8%
17 161
2.2%
18 159
2.2%
19 265
3.6%
20 311
4.2%
ValueCountFrequency (%)
69 4
 
0.1%
67 1
 
< 0.1%
66 2
 
< 0.1%
64 2
 
< 0.1%
63 5
0.1%
60 9
0.1%
59 7
0.1%
58 6
0.1%
56 5
0.1%
55 12
0.2%

CO2 Emissions(g/km)
Real number (ℝ)

High correlation 

Distinct331
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean250.5847
Minimum96
Maximum522
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2025-08-18T14:20:24.700763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum96
5-th percentile169
Q1208
median246
Q3288
95-th percentile354
Maximum522
Range426
Interquartile range (IQR)80

Descriptive statistics

Standard deviation58.512679
Coefficient of variation (CV)0.2335046
Kurtosis0.47880085
Mean250.5847
Median Absolute Deviation (MAD)40
Skewness0.52609381
Sum1850568
Variance3423.7336
MonotonicityNot monotonic
2025-08-18T14:20:24.773633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
242 85
 
1.2%
221 82
 
1.1%
230 77
 
1.0%
214 77
 
1.0%
294 76
 
1.0%
232 76
 
1.0%
258 75
 
1.0%
253 75
 
1.0%
246 75
 
1.0%
209 74
 
1.0%
Other values (321) 6613
89.5%
ValueCountFrequency (%)
96 4
0.1%
99 1
 
< 0.1%
102 1
 
< 0.1%
103 1
 
< 0.1%
104 2
 
< 0.1%
105 3
< 0.1%
106 2
 
< 0.1%
108 2
 
< 0.1%
109 2
 
< 0.1%
110 7
0.1%
ValueCountFrequency (%)
522 3
< 0.1%
493 2
< 0.1%
488 1
 
< 0.1%
487 1
 
< 0.1%
485 1
 
< 0.1%
476 1
 
< 0.1%
473 1
 
< 0.1%
467 1
 
< 0.1%
465 3
< 0.1%
464 2
< 0.1%

Interactions

2025-08-18T14:20:21.906285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:18.878177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:19.494739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:19.952241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:20.425066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:20.907161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:21.371273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:21.974327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:18.960881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:19.566610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:20.015355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:20.491375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:20.974685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:21.449112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:22.045061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:19.037444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:19.625629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:20.083866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:20.567151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:21.040739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:21.526721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:22.108808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:19.103420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:19.689051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:20.148271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:20.624655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:21.118107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:21.607356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:22.186819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:19.185522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:19.751637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:20.221528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:20.702952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:21.183469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:21.673644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:22.291415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:19.252953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:19.808689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:20.286181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:20.766234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:21.241510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:21.756780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:22.367154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:19.323730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:19.887517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:20.358384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:20.841464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:21.307488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:20:21.832430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-08-18T14:20:24.845801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
CO2 Emissions(g/km)CylindersEngine Size(L)Fuel Consumption City (L/100 km)Fuel Consumption Comb (L/100 km)Fuel Consumption Comb (mpg)Fuel Consumption Hwy (L/100 km)Fuel TypeMakeTransmissionVehicle Class
CO2 Emissions(g/km)1.0000.8520.8690.9560.963-0.9620.9410.1640.3810.2620.285
Cylinders0.8521.0000.9360.8470.834-0.8330.7820.1810.6250.2460.286
Engine Size(L)0.8690.9361.0000.8720.862-0.8610.8160.2420.4990.2800.262
Fuel Consumption City (L/100 km)0.9560.8470.8721.0000.994-0.9930.9490.3250.3550.2730.291
Fuel Consumption Comb (L/100 km)0.9630.8340.8620.9941.000-0.9990.9770.3350.3370.2700.297
Fuel Consumption Comb (mpg)-0.962-0.833-0.861-0.993-0.9991.000-0.9760.3300.2960.2770.262
Fuel Consumption Hwy (L/100 km)0.9410.7820.8160.9490.977-0.9761.0000.3320.2770.2540.311
Fuel Type0.1640.1810.2420.3250.3350.3300.3321.0000.4470.3530.296
Make0.3810.6250.4990.3550.3370.2960.2770.4471.0000.4180.359
Transmission0.2620.2460.2800.2730.2700.2770.2540.3530.4181.0000.309
Vehicle Class0.2850.2860.2620.2910.2970.2620.3110.2960.3590.3091.000

Missing values

2025-08-18T14:20:22.459638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-18T14:20:22.577171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

MakeModelVehicle ClassEngine Size(L)CylindersTransmissionFuel TypeFuel Consumption City (L/100 km)Fuel Consumption Hwy (L/100 km)Fuel Consumption Comb (L/100 km)Fuel Consumption Comb (mpg)CO2 Emissions(g/km)
0ACURAILXCOMPACT2.04AS5Z9.96.78.533196
1ACURAILXCOMPACT2.44M6Z11.27.79.629221
2ACURAILX HYBRIDCOMPACT1.54AV7Z6.05.85.948136
3ACURAMDX 4WDSUV - SMALL3.56AS6Z12.79.111.125255
4ACURARDX AWDSUV - SMALL3.56AS6Z12.18.710.627244
5ACURARLXMID-SIZE3.56AS6Z11.97.710.028230
6ACURATLMID-SIZE3.56AS6Z11.88.110.128232
7ACURATL AWDMID-SIZE3.76AS6Z12.89.011.125255
8ACURATL AWDMID-SIZE3.76M6Z13.49.511.624267
9ACURATSXCOMPACT2.44AS5Z10.67.59.231212
MakeModelVehicle ClassEngine Size(L)CylindersTransmissionFuel TypeFuel Consumption City (L/100 km)Fuel Consumption Hwy (L/100 km)Fuel Consumption Comb (L/100 km)Fuel Consumption Comb (mpg)CO2 Emissions(g/km)
7375VOLVOS90 T6 AWDMID-SIZE2.04AS8Z11.37.59.629223
7376VOLVOV60 T5STATION WAGON - SMALL2.04AS8Z10.57.18.932208
7377VOLVOV60 T6 AWDSTATION WAGON - SMALL2.04AS8Z11.07.49.430219
7378VOLVOV60 CC T5 AWDSTATION WAGON - SMALL2.04AS8Z10.87.79.430220
7379VOLVOXC40 T4 AWDSUV - SMALL2.04AS8X10.27.59.031210
7380VOLVOXC40 T5 AWDSUV - SMALL2.04AS8Z10.77.79.430219
7381VOLVOXC60 T5 AWDSUV - SMALL2.04AS8Z11.28.39.929232
7382VOLVOXC60 T6 AWDSUV - SMALL2.04AS8Z11.78.610.327240
7383VOLVOXC90 T5 AWDSUV - STANDARD2.04AS8Z11.28.39.929232
7384VOLVOXC90 T6 AWDSUV - STANDARD2.04AS8Z12.28.710.726248

Duplicate rows

Most frequently occurring

MakeModelVehicle ClassEngine Size(L)CylindersTransmissionFuel TypeFuel Consumption City (L/100 km)Fuel Consumption Hwy (L/100 km)Fuel Consumption Comb (L/100 km)Fuel Consumption Comb (mpg)CO2 Emissions(g/km)# duplicates
598LEXUSGS FCOMPACT5.08AS8Z14.99.712.5232935
187CHRYSLER300FULL-SIZE3.66A8X12.47.810.3272424
190CHRYSLER300 AWDFULL-SIZE3.66A8X12.88.711.0262584
276FIAT500LSTATION WAGON - SMALL1.44A6X10.77.99.4302214
610LEXUSNX 300h AWDSUV - SMALL2.54AV6X7.27.97.5381764
613LEXUSRC FSUBCOMPACT5.08AS8Z15.29.512.6222894
615LEXUSRX 350 AWDSUV - SMALL3.56AS8X12.29.010.8262524
618LEXUSRX 450h AWDSUV - STANDARD3.56AV6Z7.58.47.9361854
765MITSUBISHIRVR 4WDSUV - SMALL2.04AV6X10.18.29.2312134
767NISSAN370ZTWO-SEATER3.76AS7Z12.69.311.1252614